Abstract [en]

It is increasingly recognized that even cities with severe congestion during peak hours have available road capacity during nights, evenings and early mornings. Policies that shift urban goods deliveries from peak hours to off-peak hours have the potential to increase the efficiency of freight distribution, as well as to reduce negative external impacts. Between 2014 and 2016, the City of Stockholm ran a pilot project allowing inner city goods deliveries with heavy trucks at night. This paper evaluates the transport efficiency impacts of the Stockholm off-peak pilot. An evaluation framework is defined where transport efficiency is studied in a number of dimensions, including driving efficiency, delivery reliability, energy efficiency and service efficiency. For each dimension, performance indicators are introduced and evaluated. Vehicle GPS probe data, fleet management data, and logistic information are used to assess the impacts. The results suggest that shifting deliveries from daytime peak hours to night-time achieved better transport efficiency in driving efficiency, delivery reliability and energy efficiency. Meanwhile, there were no clear efficiency gains from moving deliveries from mid-day hours. For cities with varying congestion during daytime like Stockholm, the results suggest that night-time deliveries mainly increase the scheduling flexibility of carriers and recipients through the introduction of additional off-peak hours. The conclusions provide input to planners, decision-makers and local authorities to design and implement effective policy initiatives.

Abstract [en]

The paper proposes a methodology for the identification of workstations in earthwork operations based on GPS traces from construction vehicles. The model incorporates relevant information extracted from the GPS data to infer locations of different workstations as probability distributions over the environment. Monitoring of workstation locations may support map inference for generating and continuously updating the layout and road network topology of the construction environment. A case study is conducted at a complex earthwork site in Sweden. The workstation identification methodology is used to infer the locations of loading stations based on vehicle speeds and interactions between vehicles, and the locations of dumping stations based on vehicle turning patterns. The results show that the proposed method is able to identify workstations in the earthwork environment efficiently and in sufficient detail.

Pernestål Brenden, Anna

Koutoulas, Anastasios

KTH, School of Architecture and the Built Environment (ABE), Transport Science, System Analysis and Economics.

Fu, Jiali

KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.

Rumpler, Romain

KTH, School of Engineering Sciences (SCI), Aeronautical and Vehicle Engineering.

Sanchez-Diaz, Ivan

Chalmers University of Technology.

Behrends, Sönke

Chalmers University of Technology.

Glav, Ragnar

Scania CV AB.

Cederstav, Fredrik

Volvo AB.

Brolinsson, Märta

Stockholms Stad.

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2017 (English)Report (Other academic)

Abstract [en]

Two heavy trucks have been operated in Stockholm city center during night time for e period of one and a half years. New technology has been tested: one the trucks was an electric hybrid with zone management and one was a PIEK certified biogas truck. The two trucks have been operated in different delivery schemes: on dedicated and one consolidated. The off-peak trial has been assessed in from four different perspectives: noise, transport efficiency, users and policy, and socioeconomic aspects. In addition, a literature survey has been performed.

Noise produced while travelling with the two trucks tested is not disturbing. The main challenge is noise produced during unloading, and in particular in areas where the background noise is low.

Transportation efficiency is improved from several perspectives compared with daytime deliveries: transport speed increased, fuel consumption decreased and service times decreased. However, one conclusion from the project is that it is challenging to compare daytime deliveries with off-peak deliveries for an individual truck, since the routing will be different depending on the time of the day even if the delivery points are the same. The reason is that the routing during daytime will be optimized to take congestion into account. Therefore, if general conclusions are to be drawn, data from more different trucks in different delivery schemes need to be collected and analyzed.

Stakeholder interviews showed that the most important benefits are increased efficiency, shorter travel and deliver times, higher productivity both for carriers and receivers, less environmental impacts and fuel cost savings, as well as better working conditions when trucks are moved from rush hours to off-peak hours. The most important social costs are increased noise levels and noise disturbances, additional staff, equipment and wage costs as well as higher risks in handling goods deliveries at night times, especially in the case of unassisted deliveries. In general, the benefits exceed the costs.

From the socio-economic analysis it is clear that the dominating type of external cost for daytime deliveries is contribution to congestion. This cost is reduced is nearly eliminated during off-peak deliveries. In addition, off-peak deliveries reduces CO2 emissions, but even more the emissions of air pollutants and can therefore contribute significantly to improving local air quality. The cost of noise is more than twice as big as for daytime deliveries.

From the city’s perspective the most important remaining challenges are related to 1) Noise measurements and surveillance, 2) general requirements and surveillance, for example concerning vehicles, fuels, and emission levels that are to be allowed, 3) The responsibility for potential additional costs related to infrastructural changes needed.

The overall conclusion from the project is that the benefits from off-peak deliveries exceed the costs. The results from the project suggest that the concept of off-peak deliveries is beneficiary in the Stockholm region, and the off-peak delivery program is suggested to continue and be scaled up to involve more vehicles and other types of goods. During the upscaling it is relevant to continue to study effects on transport efficiency, noise levels, and potential business barriers that may arise.

Projects

Funder

Note

The project has been funded by the Vinnova program "Fordonsstrategisk Forskning och Innovation", FFI (project no 2014-05598 and 2015-02338) and ended in December 2016. It has been coordinated by ITRL Integrated Transport Research Lab at KTH Royal Institute of Technology, and has been a collaboration with the City of Stockholm, Svebol Logistics AB, Lidl Sverige KB, Martin&Servera AB, K.Hartwall Oy AB, Scania CV AB, AB Volvo and Chalmers.

Haris, Koutsopoulos

Abstract [en]

The paper presents a methodology for using GPS probe data to automatically extract the driving time between workstations and build a detailed representation of the paths between workstations in a construction environment. The inferred driving time distribution is aimed as input to construction simulation models to assess fleet performance, while the path information can be utilized to examine the performance of individual vehicles. A case study, using GPS data collected from a construction site, is used to demonstrate the capability of the proposed approach. The GPS data are processed without any prior knowledge about the underlying work environment. The results show that the proposed approach is capable of accurately inferring the driving time distribution and the paths between workstations.

Fu, Jiali

KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.

Jenelius, Erik

KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.

Koutsopoulos, Haris

2016 (English)Conference paper, Published paper (Refereed)

Abstract [en]

Increasing availability and the use of Global Positioning System (GPS) devices open great opportunities for various transportation applications. The authors propose a generative probabilistic model for extraction of locations of workstations in earthwork operations using raw GPS traces from construction vehicles. The probabilistic model incorporates the GPS measurements with relevant information extracted from the GPS data to compute locations of different workstations as probability distribution over the environment. The location of workstations will be used as a part of a map inference method for generating and continuously updating the layout and road network topology of the construction environment. A detailed case study was conducted with construction equipment at a complex site. The authors first demonstrate the probabilistic model to extract the locations of loading stations using vehicle speed and interactions among vehicles, and then to discover dumping stations with help of vehicle moving patterns. The results from the experiment show that the proposed method is able to discover important places and workstations for earthwork environment efficiently and in sufficient details.